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Data Modeling with Snowflake

You're reading from   Data Modeling with Snowflake A practical guide to accelerating Snowflake development using universal modeling techniques

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Product type Paperback
Published in Sep 2025
Publisher Packt
ISBN-13 9781837028030
Length 432 pages
Edition 2nd Edition
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Author (1):
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Serge Gershkovich Serge Gershkovich
Author Profile Icon Serge Gershkovich
Serge Gershkovich
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Toc

Table of Contents (27) Chapters Close

Preface 1. Part 1: Core Concepts in Data Modeling and Snowflake Architecture
2. Unlocking the Power of Modeling FREE CHAPTER 3. An Introduction to the Four Modeling Types 4. Mastering Snowflake’s Architecture 5. Mastering Basic Snowflake Objects 6. From Logical Concepts to Snowflake Objects 7. Mastering Advanced Snowflake Objects 8. Seeing Snowflake’s Architecture Through Modeling Notation 9. Part 2: Applied Modeling from Idea to Deployment
10. Putting Conceptual Modeling into Practice 11. Putting Logical Modeling into Practice 12. Database Normalization 13. Database Naming and Structure 14. Putting Physical Modeling into Practice 15. Part 3: Solving Real-World Problems with Transformational Modeling
16. Putting Transformational Modeling into Practice 17. Modeling Slowly Changing Dimensions 18. Modeling Facts for Rapid Analysis 19. Modeling Semi-Structured Data 20. Modeling Hierarchies 21. Part 4: Data Modeling for Enterprise Teams
22. Scaling Data Models through Modern Techniques 23. Unlocking Value for Enterprise Organizations 24. Other Books You May Enjoy
25. Index
Appendix

Database Normalization

Previous chapters have explored the method of capturing the real-world business workings of an organization and modeling them using visual semantics. The resulting model and accompanying diagrams make it easy for the domain and data teams to reach a consensus about the business’s fundamental entities and the interactions between them. However, as the modeling process approaches the physical stage, we should understand that many ways exist to structure the data at the database level. The process of dividing the data into smaller, modularized segments is known as normalization. In this chapter, we will understand how it works and the advantages and disadvantages that go along with it.

Normalization is not a binary classification but a spectrum of ever-increasing rules a design must satisfy to achieve a stated level. While normalization contains the root word normal, along with the obvious positive connotations, more normalization does not necessarily...

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